Virtual colonoscopy is a noninvasive technique for human colon cancer screening. Computed tomography (CT) or Magnetic resonance (MR) techniques are used to generate high-resolution cross-sectional images of the inner surface of the colon. The techniques are presently of importance in the field of medicine.
The technique of Virtual Colonoscopy is utilized to search the internal surface of the colon for polyps, using CT or MR data and computer graphics techniques, instead of performing an actual endoscopic examination with a real endoscope. This is desirable in order to reduce the necessity of performing an actual endoscopic examination and thereby reduce the need for patient preparation, patient discomfort, and any risk attendant upon such procedures. A “virtual endoscope” or camera is virtually directed through the colon, looking at the surface thereof to detect bumps that may indicate the presence of polyps. It is important to know whether the entire surface has been observed for examination, or whether there are patches or areas, typically concealed behind folds, that have not been seen or observed. Such patches could contain a polyp which would otherwise be missed in the virtual colonoscopy examination.
It has been described in the literature how such unseen patches can be detected in the case of a virtual three-dimensional (3D) flythrough; see, for example, A method for detecting unobserved regions in a virtual endoscopy system, Y. Hayashi, K. Mori, J. Toriwaki, Y. Suenaga, J. Hasegawa, Nagoya Univ. (J) CARS June 2001 (also SPIE may 2001)
Both CT and MR colonography generate a large number of images that must be interpreted by a radiologist for the presence of polyps; see Arie E. Kaufman, Sarang Lakare, Kevin Kreeger, Ingmar Bitter, Virtual Colonoscopy, Communications of the ACM, vol 48, No. 2, pp. 37-41, 2005.
Commonly used methods to examine these datasets include slice-by-slice viewing referred to as primary 2-dimensional (2D) reading and virtual flythroughs referred to as primary 3-dimensional (3D) reading. There appears to be little agreement in the literature as to which method results in the greatest rate of polyp detection; see Hara A K, Johnson C D, Reed J E, Ehman R L, Ilsrtup D M, Colorectal polyp detection with CT Colonography, two-versus three dimensional techniques, Radiology, 1996, 200:49-54.
A number of techniques have been proposed to facilitate 3D reading. Most of these techniques automate the navigation process by calculating the colonic centerline; see for example, U.S. patent application Ser. No. 10/842,972, filed May 11, 2004 in the name of Boissonnat, Jean-Daniel and Geiger, Bernhard and entitled METHOD AND APPARATUS FOR FAST AUTOMATIC CENTERLINE EXTRACTION FOR VIRTUAL ENDOSCOPY whereof the disclosure is incorporated herein by reference; Robert J. T. Sadleir, Paul F. Whelan, Colon Centerline Calculation for CT Colonography using Optimised 3D Topological Thinning, 1st International Symposium on 3D Data Processing Visualization and Transmission (3DPVT'02), pp. 800-804, 2002; I. Bitter, M. Sato, M. Bender, A. Kaufman, M. Wan, A Smooth, Accurate and Robust Centerline Extraction Algorithm, In Proc. of IEEE Visualisation, 2000; R. Chiou, A. Kaufman, Z. Liang, L. Hong, and M. Achniotou, Interactive Fly-Path Planning Using Potential Fields and Cell Decomposition for Virtual Endoscopy,” IEEE Trans Nuclear Sciences, vol. 46, no. 4, pp. 1045-1049, 1999; and Samara Y, Fiebich M, Dachman A H, Kuniyoshi J K, Doi K, Hoffmann K R., Automated calculation of the centerline of the human colon on CT images, Acad Radiol. 1999 June; 6(6): 352-9. Other techniques automate the navigation process by computing the longest ray cast along the view direction. See, for example, U.S. patent application Ser. No. 10/322,326, filed Dec. 18, 2002 in the name of B. Geiger, and entitled AUTOMATIC NAVIGATION FOR VIRTUAL ENDOSCOPY; whereof the disclosure is incorporated herein by reference.
Another valuable help for 3D reading is the availability of techniques to get a map of colon wall patches that have not been observed during flythrough. Frequently, such areas are between deep Haustral folds. Such techniques have been proposed by, for example, F. M. Vos et. al. “A new visualization method for virtual colonoscopy”, Lecture Notes in Computer Science, vol. 2208, 2001. However, these techniques are limited to 3D flythrough.
This is typically accomplished in three steps:                (a) The entire surface of the colon is calculated, by using colon segmentation techniques: (see, for example, the cited literature on colon segmentation below);        (b) during flythrough, all surface parts that have been rendered in the endoscopic view are marked as “seen”; and        (c) after flythrough has been completed, or at some other convenient time, the seen areas are subtracted from the complete surface, and the remaining “unseen” patches are calculated and displayed in a useful way.        
Additional Information on centerline derivation techniques is provided in above-cited documents. See the above-cited publications by Robert, J. T. et al.; I. Bitter et al.; Samara, Y. et al.; Frimmel, H. et al.; and K. Mori et al.
Additional material on colon segmentation can be found in, for example, M. Sato, S. Lakare, M. Wan, A. Kaufman, Z. Liang, and M. Wax (2001) “An automatic colon segmentation for 3D virtual colonoscopy,” IEICE Trans. Information and Systems, Vol. E84-D, No. 1, January 2001, pp. 201-208; and D. Chen, Z. Liang, M. Wax, Lihong Li, B. Li, and A. Kaufman (2000) “A Novel Approach to Extract Colon Lumen from CT Images for Virtual Colonoscopy,” IEEE Transactions on Medical Imaging, Vol. 19, No. 12, December 2000, pp. 1220-1226.
Further basic information on segmentation, connected components, surface rendering and related topics can be found in a number of textbooks such as, for example, VIRTUAL ENDOSCOPY AND RELATED 3D TECHNIQUES, edited by A. L. Baert; Springer, New York; 2001, 2002; FUNDAMENTALS OF IMAGE PROCESSING by Arthur R. Weeks, SPIE Optical Engineering Press & IEEE Press; 1996; IMAGE PROCESSING, ANALYSIS, AND MACHINE VISION, Second Edition, by Milan Sonka et al., PWS Publishing; 1999; and DIGITAL IMAGE PROCESSING, Second Edition, by Rafael C. Gonzalez et al., Prentice Hall; 2002.
However, the foregoing calculation in steps (a), (b), and (c) was heretofore only available for (3D) endoscopic flythrough and was limited thereto.